--- title: "athina-evals vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-onejune2018-awesome-llm-eval" tools: ["athina-ai-athina-evals", "onejune2018-awesome-llm-eval"] --- # athina-evals vs Awesome-LLM-Eval *GraphCanon updated Jul 28, 2026* ## Verdict Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 654 stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Curated list for evaluation of large language models | | Stars | 301 | 654 | | Forks | 22 | 82 | | Open issues | 3 | 44 | | Language | Python | - | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 417d | 246d | | Open issues (now) | 3 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## Decision facts: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Decision facts: Awesome-LLM-Eval - **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms. - **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications. - **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. ## Choose when ### Choose athina-evals if… - Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation-toolkit. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - Leaner open-issue backlog (3). ### Choose Awesome-LLM-Eval if… - Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.. - Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.. - Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, large language models. - When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ## When NOT to use athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## When NOT to use Awesome-LLM-Eval - You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. - If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ## Common questions ### What is the difference between athina-evals and Awesome-LLM-Eval? athina-evals: Python SDK for evaluating LLM generated responses. Awesome-LLM-Eval: Curated list for evaluation of large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over Awesome-LLM-Eval? Choose athina-evals over Awesome-LLM-Eval when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation-toolkit; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; Leaner open-issue backlog (3). ### When should I choose Awesome-LLM-Eval over athina-evals? Choose Awesome-LLM-Eval over athina-evals when Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, large language models; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ### When should I avoid athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### When should I avoid Awesome-LLM-Eval? You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ### Is athina-evals or Awesome-LLM-Eval more popular on GitHub? Awesome-LLM-Eval has more GitHub stars (654 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/athina-ai-athina-evals-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or Awesome-LLM-Eval? athina-evals: Dormant. Awesome-LLM-Eval: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for athina-evals and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-evals) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_